Cambridge University Press
Applied Asymptotics: Case Studies in Small-Sample Statistics
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Title: Applied Asymptotics: Case Studies in Small-Sample Statistics
Author: A R Brazzale
ISBN: 9780521847032
Publisher: Cambridge University Press
Published: 2007
Binding: Hardcover
Language: English
Condition: Used: Good
Moderate edge wear. Binding good. May have marking in text. We sometimes source from libraries. We ship in recyclable American-made mailers. 100% money-back guarantee on all orders.
M 1490810
Publisher Description:
In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods. Author resource page: http: //www.isib.cnr.it/ brazzale/AA/
Author: A R Brazzale
ISBN: 9780521847032
Publisher: Cambridge University Press
Published: 2007
Binding: Hardcover
Language: English
Condition: Used: Good
Moderate edge wear. Binding good. May have marking in text. We sometimes source from libraries. We ship in recyclable American-made mailers. 100% money-back guarantee on all orders.
M 1490810
Publisher Description:
In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods. Author resource page: http: //www.isib.cnr.it/ brazzale/AA/
